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Artykuły w czasopismach na temat „Load balancing”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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1

x, Kuldeep, and Ravinder Kumar. "Load Balancing Techniques ? A Review." International Journal of Scientific Engineering and Research 5, no. 12 (December 27, 2017): 26–29. https://doi.org/10.70729/ijser172078.

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Suguna, Dr S., and R. Barani. "Simulation of Dynamic Load Balancing Algorithms." Bonfring International Journal of Software Engineering and Soft Computing 5, no. 1 (July 31, 2015): 01–07. http://dx.doi.org/10.9756/bijsesc.8061.

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3

SA MVPanduranga Rao, Kavya. "Grid Computing for Load Balancing Strategies." International Journal of Science and Research (IJSR) 1, no. 3 (March 5, 2012): 1–7. http://dx.doi.org/10.21275/ijsr12120309.

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Huang, Weihua, Zhong Ma, Xinfa Dai, Mingdi Xu, and Yi Gao. "Fuzzy Clustering with Feature Weight Preferences for Load Balancing in Cloud." International Journal of Software Engineering and Knowledge Engineering 28, no. 05 (May 2018): 593–617. http://dx.doi.org/10.1142/s021819401850016x.

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Load balancing, which redistributes dynamic workloads across computing nodes within cloud to improve resource utilization, is one of the main challenges in cloud computing system. Most existing rule-based load balancing algorithms failed to effectively fuse load data of multi-class system resources. The strategies they used for balancing loads were far from optimum since these methods were essentially performed in a combined way according to load state. In this work, a fuzzy clustering method with feature weight preferences is presented to overcome the load balancing problem for multi-class sy

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5

Chi-Chung Hui and S. T. Chanson. "Hydrodynamic load balancing." IEEE Transactions on Parallel and Distributed Systems 10, no. 11 (1999): 1118–37. http://dx.doi.org/10.1109/71.809572.

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6

Friedrich, Tobias, Martin Gairing, and Thomas Sauerwald. "Quasirandom Load Balancing." SIAM Journal on Computing 41, no. 4 (January 2012): 747–71. http://dx.doi.org/10.1137/100799216.

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7

Pearce, Olga, Todd Gamblin, Bronis R. de Supinski, Martin Schulz, and Nancy M. Amato. "Decoupled load balancing." ACM SIGPLAN Notices 50, no. 8 (December 18, 2015): 267–68. http://dx.doi.org/10.1145/2858788.2688539.

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Bonald, T., M. Jonckheere, and A. Proutiére. "Insensitive load balancing." ACM SIGMETRICS Performance Evaluation Review 32, no. 1 (June 2004): 367–77. http://dx.doi.org/10.1145/1012888.1005729.

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9

Grosof, Isaac, Ziv Scully, and Mor Harchol-Balter. "Load Balancing Guardrails." ACM SIGMETRICS Performance Evaluation Review 47, no. 1 (December 17, 2019): 9–10. http://dx.doi.org/10.1145/3376930.3376937.

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10

Grosof, Isaac, Ziv Scully, and Mor Harchol-Balter. "Load Balancing Guardrails." Proceedings of the ACM on Measurement and Analysis of Computing Systems 3, no. 2 (June 19, 2019): 1–31. http://dx.doi.org/10.1145/3341617.3326157.

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11

Alkassar, Eyad, Mark A. Hillebrand, Dirk C. Leinenbach, Norbert W. Schirmer, Artem Starostin, and Alexandra Tsyban. "Balancing the Load." Journal of Automated Reasoning 42, no. 2-4 (March 28, 2009): 389–454. http://dx.doi.org/10.1007/s10817-009-9123-z.

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12

Batumalai, Sathis Kumar, Joseph Ng Poh Soon, Choo Peng Yin, Wong See Wan, Phan Koo Yuen, and Lim Eng Heng. "IP Redundancy and Load Balancing With Gateway Load Balancing Protocol." International Journal of Scientific Engineering and Technology 4, no. 3 (March 1, 2015): 218–22. http://dx.doi.org/10.17950/ijset/v4s3/321.

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13

Boulmier, Anthony, Nabil Abdennadher, and Bastien Chopard. "Optimal load balancing and assessment of existing load balancing criteria." Journal of Parallel and Distributed Computing 169 (November 2022): 211–25. http://dx.doi.org/10.1016/j.jpdc.2022.07.002.

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14

Al-Najjar, Anees, Marius Portmann, Siamak Layeghy, and Jadwiga Indulska. "Flow-level Load Balancing of HTTP Traffic using OpenFlow." Australian Journal of Telecommunications and the Digital Economy 6, no. 4 (December 31, 2018): 75–95. http://dx.doi.org/10.18080/ajtde.v6n4.166.

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In this paper, we explore the concept of flow-based load balancing of network traffic on multi-homed hosts. In contrast to existing approaches such as MultipathTCP, our approach is a client-side-only solution, and can therefore easily be deployed. We specifically explore flow-based load balancing for web and video traffic use cases. Experimental evaluations of our OpenFlow-based load balancer demonstrate the potential of flow-based load balancing.

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15

Al-Najjar, Anees, Marius Portmann, Siamak Layeghy, and Jadwiga Indulska. "Flow-level Load Balancing of HTTP Traffic using OpenFlow." Journal of Telecommunications and the Digital Economy 6, no. 4 (December 31, 2018): 75–95. http://dx.doi.org/10.18080/jtde.v6n4.166.

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In this paper, we explore the concept of flow-based load balancing of network traffic on multi-homed hosts. In contrast to existing approaches such as MultipathTCP, our approach is a client-side-only solution, and can therefore easily be deployed. We specifically explore flow-based load balancing for web and video traffic use cases. Experimental evaluations of our OpenFlow-based load balancer demonstrate the potential of flow-based load balancing.

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16

Yadav, Ram Kumar, and Deepak Kumar. "Productivity Improvement by Load Balancing of Machines." International Journal of Scientific Research 2, no. 3 (June 1, 2012): 168–69. http://dx.doi.org/10.15373/22778179/mar2013/53.

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17

Klymyshyn, Nazarii. "The Network Load Balancer in Decentrilized Systems." Advances in Cyber-Physical Systems 8, no. 1 (May 10, 2023): 25–34. http://dx.doi.org/10.23939/acps2023.01.025.

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This article explores the implementation of network load balancing in decentralized systems using OpenWrt, Quality of Service (QoS), and traffic balancing techniques. The increasing demand for high-quality net- work services and the surge in network traffic requires the adoption of more efficient load-balancing methods to main- tain network performance. This paper discusses the use of OpenWrt, an open-source firmware for network routers, to configure and manage network traffic. The article also covers the implementation of QoS and traffic balancing techniques to optimize network performance an

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18

Fadila, Aina, Muhammad Nasir, and Safriadi Safriadi. "Implementasi Sistem Load Balancing Web Server Pada Jaringan public Cloud Computing Menggunakan Least Connection." Journal of Artificial Intelligence and Software Engineering (J-AISE) 3, no. 2 (October 28, 2023): 50. http://dx.doi.org/10.30811/jaise.v3i2.4578.

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Web adalah sebuah perangkat lunak dengan berbasis data yang berfungsi untuk menerima permintaan dari client dan tanggapan permintaan dengan mentranfer melalui browser yang merupakan halaman situs web. Dibalik kemudahan pengaksesan segala informasi terdapat permasalahan yang terjadi pada trafik yang menuju web server yaitu dengan meningkatnya permintaan informasi akan dapat menjadikan trafik menuju web server menjadi overload dan akhirnya menjadi down karena tidak mampu menjalankan permintaan yang berlebihan. Untuk mengatasi permasalahan tersebut adalah dengan menggunakan load balancing yang be

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19

NOORUZAMA, SYEDA. "LOAD BALANCING IN DISTRIBUTION SYSTEM." International Scientific Journal of Engineering and Management 04, no. 05 (May 28, 2025): 1–9. https://doi.org/10.55041/isjem03842.

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ABSTRACT: This paper presents a practical approach to achieving effective load balancing in electrical distribution systems. Load imbalance leads to energy losses, overheating, and voltage deviations, which degrade system efficiency and reliability. This study introduces a microcontroller-based monitoring system integrated with a relay switching mechanism that automatically redistributes loads across phases to maintain balance. The system uses current sensors to detect imbalance and performs real-time switching to ensure optimal performance. This design is cost-effective and particularly suite

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20

Shivaliya, Shikha, and Vijay Anand. "Design of Load Balancing Technique for Cloud Computing Environment." ECS Transactions 107, no. 1 (April 24, 2022): 2911–18. http://dx.doi.org/10.1149/10701.2911ecst.

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Cloud computing allows for the provision of IT resources on-demand and has various advantages. Because the majority of firms have shifted their activities to the cloud, data centers are frequently flooded with sporadic loads. When dealing with high network traffic in the cloud, it is necessary to balance the load among servers. This is something that load balancing can help with. The primary goal of load balancing is to distribute demand evenly among all available servers such that no server is under or overloaded. Load balancing is the process of dispersing load among several nodes to make th

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21

Sufiev, Hadar, Yoram Haddad, Leonid Barenboim, and José Soler. "Dynamic SDN Controller Load Balancing." Future Internet 11, no. 3 (March 21, 2019): 75. http://dx.doi.org/10.3390/fi11030075.

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The software defined networking (SDN) paradigm separates the control plane from the data plane, where an SDN controller receives requests from its connected switches and manages the operation of the switches under its control. Reassignments between switches and their controllers are performed dynamically, in order to balance the load over SDN controllers. In order to perform load balancing, most dynamic assignment solutions use a central element to gather information requests for reassignment of switches. Increasing the number of controllers causes a scalability problem, when one super control

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22

Maurya, Santosh Kumar, and Garima Sinha. "Load balancing in cloud computing: an analytical review and proposal." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1530–37. https://doi.org/10.11591/ijeecs.v26.i3.pp1530-1537.

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Task scheduling or mapping of requests to machines with specified requirements becomes a very important problem to get optimized results. With the requests of many users, the machine can go under different conditions such as heavy load and minimum load. These various aspects can lead to system failure and loss of data. Hence the load must be balanced to beat the losses of the system. A load balancing concept described how different types of loads could be balanced with each machine. The load can be of memory, computation, and network. The paper mainly focuses on all kinds of load to provide a

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23

Mekonnen, Dawit, Alemayehu Megersa, Rakesh Kumar Sharma, and Durga Prasad Sharma. "Designing a Component-Based Throttled Load Balancing Algorithm for Cloud Data Centers." Mathematical Problems in Engineering 2022 (October 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/4640443.

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Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing alg

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24

Liu, Zhenhua, Minghong Lin, Adam Wierman, Steven Low, and Lachlan L. H. Andrew. "Greening Geographical Load Balancing." IEEE/ACM Transactions on Networking 23, no. 2 (April 2015): 657–71. http://dx.doi.org/10.1109/tnet.2014.2308295.

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25

Hofmeyr, Steven, Costin Iancu, and Filip Blagojević. "Load balancing on speed." ACM SIGPLAN Notices 45, no. 5 (May 2010): 147–58. http://dx.doi.org/10.1145/1837853.1693475.

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26

Anselmi, J., and N. S. Walton. "Decentralized Proportional Load Balancing." SIAM Journal on Applied Mathematics 76, no. 1 (January 2016): 391–410. http://dx.doi.org/10.1137/140969361.

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27

Hiraishi, Tasuku, Masahiro Yasugi, Seiji Umatani, and Taiichi Yuasa. "Backtracking-based load balancing." ACM SIGPLAN Notices 44, no. 4 (February 14, 2009): 55–64. http://dx.doi.org/10.1145/1594835.1504187.

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28

Doroudi, Sherwin, Esa Hyytiä, and Mor Harchol-Balter. "Value driven load balancing." Performance Evaluation 79 (September 2014): 306–27. http://dx.doi.org/10.1016/j.peva.2014.07.019.

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29

Nahir, Amir, Ariel Orda, and Danny Raz. "Replication-Based Load Balancing." IEEE Transactions on Parallel and Distributed Systems 27, no. 2 (February 1, 2016): 494–507. http://dx.doi.org/10.1109/tpds.2015.2400456.

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30

Canright, Geoffrey, Andreas Deutsch, and Tore Urnes. "Chemotaxis-Inspired Load Balancing." Complexus 3, no. 1-3 (2006): 8–23. http://dx.doi.org/10.1159/000094184.

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31

Delgosha, Payam, and Venkat Anantharam. "Load Balancing in Hypergraphs." Journal of Statistical Physics 173, no. 3-4 (March 7, 2018): 546–625. http://dx.doi.org/10.1007/s10955-018-1977-1.

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32

Buchbinder, Niv, and Joseph Naor. "Fair online load balancing." Journal of Scheduling 16, no. 1 (March 10, 2011): 117–27. http://dx.doi.org/10.1007/s10951-011-0226-0.

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33

Lee, Kangbok, Joseph Y. T. Leung, and Michael L. Pinedo. "Two dimensional load balancing." Operations Research Letters 42, no. 8 (December 2014): 539–44. http://dx.doi.org/10.1016/j.orl.2014.09.006.

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34

Kamble, Sachin, and R. H. Borhade. "Load Balancing Through Multipathing." International Journal of Engineering Trends and Technology 36, no. 5 (June 25, 2016): 219–23. http://dx.doi.org/10.14445/22315381/ijett-v36p241.

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35

Kaluzny, Bohdan L., and R. H. A. David Shaw. "Optimal aircraft load balancing." International Transactions in Operational Research 16, no. 6 (November 2009): 767–87. http://dx.doi.org/10.1111/j.1475-3995.2009.00723.x.

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36

Berenbrink, Petra, Tom Friedetzky, Leslie Ann Goldberg, Paul W. Goldberg, Zengjian Hu, and Russell Martin. "Distributed Selfish Load Balancing." SIAM Journal on Computing 37, no. 4 (January 2007): 1163–81. http://dx.doi.org/10.1137/060660345.

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Zhou, Xingyu. "Asymptotically Optimal Load Balancing." ACM SIGMETRICS Performance Evaluation Review 47, no. 3 (January 23, 2020): 34–37. http://dx.doi.org/10.1145/3380908.3380919.

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Azar, Yossi, Andrei Z. Broder, and Anna R. Karlin. "On-line load balancing." Theoretical Computer Science 130, no. 1 (August 1994): 73–84. http://dx.doi.org/10.1016/0304-3975(94)90153-8.

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39

Adler, Micah, Soumen Chakrabarti, Michael Mitzenmacher, and Lars Rasmussen. "Parallel randomized load balancing." Random Structures and Algorithms 13, no. 2 (September 1998): 159–88. http://dx.doi.org/10.1002/(sici)1098-2418(199809)13:2<159::aid-rsa3>3.0.co;2-q.

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Badshaha, P. Mulla, Rama Krishna C., and Kumar Tickoo Raj. "Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud." International Journal of Computer Networks & Communications (IJCNC) 12, no. 1 (March 16, 2020): 83–96. https://doi.org/10.5281/zenodo.3712124.

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Cloud computing is a growing service computing trend that offers users a range of on-demand variety of services from applications, processing capability, and storage based on the concept of the “Pay-As-PerUse” model. Organizations from every sector are now realizing the benefits offered by cloud computing technology and moving towards the cloud. Cloud computing offers numerous advantages over conventional computing. However, it still faces a few challenges such as resource utilization in a cloud data centre and quality of service to the end-users due to improper workload balance am

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41

Maurya, Santosh Kumar, and Garima Sinha. "Load balancing in cloud computing: an analytical review and proposal." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1530. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1530-1537.

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Task scheduling <span>or mapping of requests to machines with specified requirements becomes a very important problem to get optimized results. With the requests of many users, the machine can go under different conditions such as heavy load and minimum load. These various aspects can lead to system failure and loss of data. Hence the load must be balanced to beat the losses of the system. A load balancing concept described how different types of loads could be balanced with each machine. The load can be of memory, computation, and network. The paper mainly focuses on all kinds of load t

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42

Mishra, Swati, and Sanjaya Kumar Panda. "An Efficient Server Minimization Algorithm for Internet Distributed Systems." International Journal of Rough Sets and Data Analysis 4, no. 4 (October 2017): 17–30. http://dx.doi.org/10.4018/ijrsda.2017100102.

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The increasing use of online services leads to an unequal distribution of the loads among the servers. As a result, the problem is to balance the loads among the servers such that the total number of active servers is minimized. One of the possible solutions is to transfer the loads from the underutilized server to a suitable server and make the underutilized server to sleep mode. In this paper, a server minimization algorithm (SMA) is proposed for the solution of server minimization and the load balancing problem. The proposed algorithm reduces the number of servers by merging the loads of th

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43

BOKIYE, Lencho M., and Ilker Ali OZKAN. "HYBRID LOAD BALANCING POLICY TO OPTIMIZE RESOURCE DISTRIBUTION AND RESPONSE TIME IN CLOUD ENVIRONMENT." International Journal of Applied Mathematics Electronics and Computers 10, no. 4 (December 31, 2022): 101–9. http://dx.doi.org/10.18100/ijamec.1158866.

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Load balancing and task scheduling are the main challenges in Cloud Computing. Existing load balancing algorithms have a drawback in considering the capacity of virtual machines while distributing loads among them. The proposed algorithm works toward solving existing issues, such as fair load distribution, avoiding underloading and overloading, and improving response time. It implements best practices of Throttled load balancing algorithm and Equally Shared Current Execution algorithm. Virtual machines are selected based on the ratio of their bandwidth and load allocation count. Requests are s

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44

Mondal, Ranjan Kumar, Enakshmi Nandi, and Debabrata Sarddar. "Load Balancing Scheduling with Shortest Load First." International Journal of Grid and Distributed Computing 8, no. 4 (August 31, 2015): 171–78. http://dx.doi.org/10.14257/ijgdc.2015.8.4.17.

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45

Ri, Sotetsu, Yusheng Ji, Shoichiro Asano, and Jun Matsukata. "Load balancing method based on load vector." Systems and Computers in Japan 25, no. 2 (1994): 8–21. http://dx.doi.org/10.1002/scj.4690250202.

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46

Aulina, Nida, and Mochammad Machlul Alamin. "Implementation of Internet Network Load Balancing at the Faculty of Computer Science UNUSIDA Using the ECMP Method." JEECS (Journal of Electrical Engineering and Computer Sciences) 10, no. 1 (June 1, 2025): 59–68. https://doi.org/10.54732/jeecs.v10i1.7.

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A stable internet speed is essential in supporting worker and student task completion. Slow internet connectivity can become a persistent hindrance if it occurs continuously. Previous research indicates that conventional load balancing methods still have limitations in handling uneven traffic loads. This study, therefore, explores methods to optimize internet link utilization by distributing traffic load evenly, known as load balancing, using the Equal Cost Multi-Path (ECMP) method. This approach aims to maintain maximum data/packet flow, thereby preventing overload on a single link or connect

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47

Ullah, Ibrar, Irshad Hussain, Khalid Rehman, Piotr Wróblewski, Wojciech Lewicki, and Balasubramanian Prabhu Kavin. "Exploiting the Moth–Flame Optimization Algorithm for Optimal Load Management of the University Campus: A Viable Approach in the Academia Sector." Energies 15, no. 10 (May 19, 2022): 3741. http://dx.doi.org/10.3390/en15103741.

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Unbalanced load condition is one of the major issues of all commercial, industrial and residential sectors. Unbalanced load means that, when different loads are distributed on a three-phase four-wire system, unequal currents pass through the three phases. Due to it, a heavy current flows in the neutral wire, which not only adds the losses, but also puts constraints on three phases’ loads. In this paper, we have presented a practical approach for load balancing. First, we have considered the existing three-phase load system where the supply is a three-phase unbalanced supply. Before balancing t

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48

Haider, Syed Ehsan, Muhammad Faizan Khan, and Yousaf Saeed. "Adaptive Load Balancing Approach to Mitigate Network Congestion in VANETS." Computers 13, no. 8 (August 13, 2024): 194. http://dx.doi.org/10.3390/computers13080194.

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Load balancing to alleviate network congestion remains a critical challenge in Vehicular Ad Hoc Networks (VANETs). During route and response scheduling, road side units (RSUs) risk being overloaded beyond their calculated capacity. Despite recent advancements like RSU-based load transfer, NP-Hard hierarchical geography routing, RSU-based medium access control (MAC) schemes, simplified clustering, and network activity control, a significant gap persists in employing a load-balancing server for effective traffic management. We propose a server-based network congestion handling mechanism (SBNC) i

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49

Kumar Singh Sandeep, Ashish. "Scheduling Algorithm with Load Balancing in Cloud Computing." International Journal of Scientific Engineering and Research 2, no. 1 (January 27, 2014): 38–43. https://doi.org/10.70729/j201391.

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50

Kaur, Manpreet, and Amandeep Kaur. "Load Balancing in Cloud Computing Using Optimization Algorithm." International Journal of Science and Research (IJSR) 10, no. 9 (September 27, 2021): 939–45. https://doi.org/10.21275/mr21912194450.

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